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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "Python 3.6.9 64-bit",
   "display_name": "Python 3.6.9 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# 线性回归模型预测血糖数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import scipy.io as scio\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import csv"
   ]
  },
  {
   "source": [
    "## 读取数据\n",
    "\n",
    "数据格式为mat文件，其中`PPP`标签为和血糖相关的系数，暂时不明了具体是哪些系数，而`TTT`则是表示着对应的血糖，提取的数据如下所示，数据量较小，仅有205组数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"data.mat\"\n",
    "data = scio.loadmat(file_name)\n",
    "X = data.get(\"PPP\")\n",
    "X = X.T\n",
    "X = (X-X.mean(0))/X.std(0)\n",
    "y = data.get(\"TTT\")\n",
    "y = y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X.shape : (205, 4)  y.shape:  (205,)\n"
     ]
    }
   ],
   "source": [
    "# 确认X和y数据第一维一致\n",
    "print(\"X.shape :\", X.shape, \" y.shape: \", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 0.58313996, -1.32768467, -2.03085591, -1.18108895],\n",
       "       [-0.56238481,  0.29455256,  0.16250569,  0.52500084],\n",
       "       [-0.6851196 ,  0.07825426, -0.2189485 ,  0.99958835],\n",
       "       [ 0.41949356, -0.31829262,  1.08435332,  0.20240034],\n",
       "       [-0.8078544 , -0.13804403,  1.05256547, -1.2337239 ],\n",
       "       [ 0.05128918,  1.15974574, -0.31431205,  2.61113739],\n",
       "       [-1.54426318, -1.54398296, -0.31431205,  1.03802286],\n",
       "       [-1.46243998,  0.54690057,  1.11614117,  0.82227804],\n",
       "       [-0.27600362, -0.0659446 , -1.42688677, -0.73749783]])"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "X[1:10,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([6.6, 9.1, 5.1, 5.3, 4.6, 6.3, 8.1, 4.7, 5.2])"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "y[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[:,1],y, s=2)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 创建测试集\n",
    "\n",
    "再确认数据格式无误后，可以将数据集分为训练数据集和测试数据集\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据比例划分数据集为训练集和测试集\n",
    "def split_train_test(X, y, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(y))\n",
    "    test_set_size = int(len(y) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return X[train_indices,:], y[train_indices], X[test_indices,:], y[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分割得到训练集和测试集\n",
    "train_X, train_y, test_X, test_y = split_train_test(X, y, 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "((123, 4), (123,), (82, 4), (82,))"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "train_X.shape, train_y.shape,test_X.shape, test_y.shape"
   ]
  },
  {
   "source": [
    "## 训练数据集\n",
    "\n",
    "这里选择简单的线性回归模型，采用梯度下降的方法训练数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(train_X, train_y)"
   ]
  },
  {
   "source": [
    "以上就是简单的线性回归训练，从原始数据中，随机挑选几个数据检验模型的预测效果"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Predictions: [5.42779451 6.32162845 6.03767898 6.14878668 6.06017516 5.25149279\n 7.03252987 5.55994194 5.56920587 6.14756084 6.63649279 5.60324435\n 5.82904361 5.29034496 6.84098923 6.27749076 6.83436673 5.36533883\n 5.25740834 6.51156973 6.08467095 5.74484976 6.14203751 5.76613131\n 6.287307   6.20744982 5.34692912 6.52194592 5.7576547  5.36597283\n 6.08913836 7.93847676 5.40006694 6.13923634 6.1815335  6.39100611\n 6.99271304 6.35488924 6.28871558 5.95981579 5.70397027 6.26636945\n 7.73030027 5.76923813 5.83639958 5.69428159 6.05521163 7.52287791\n 5.92402263 5.82974519 7.0659224  5.57451558 5.71799787 5.48239141\n 6.74810574 5.74375887 6.11630808 6.97028041 6.26018177 6.94710203\n 5.67497141 6.37459078 6.23047672 5.77427038 5.3645231  6.22084405\n 5.73938485 5.73348764 5.67440196 7.151381   5.78061506 5.323685\n 6.59825572 6.44669482 5.59223054 5.85463837 6.98599284 7.31640824\n 5.88369919 6.15257912 6.17250547 5.59312976 6.39786386 6.064114\n 5.90855819 5.28770225 6.21645755 6.19718958 5.80073205 5.74067493\n 5.93182927 6.16201658 5.14413958 5.22594834 6.78098494 7.28848932\n 5.76860905 5.42085659 5.57235972 5.80230347 6.50749801 6.17600079\n 6.10982357 5.88716362 6.12810322 6.28771627 5.40133789 6.16989559\n 6.20366505 6.42895966 5.1931825  5.96136659 6.78776902 7.0913932\n 5.19130882 5.95003499 5.53276868 7.30103065 5.46984089 6.31052673\n 5.99866956 6.17829616 5.56288643 7.3108468  5.36573984 5.16099337\n 5.81297218 5.78717469 6.27840679 6.02678801 7.83913398 6.99330864\n 5.88269399 6.0413542  7.50473667 5.48751431 5.67074926 6.26388532\n 6.37651381 5.64426515 6.36815563 5.55529561 6.75306569 5.74776595\n 5.77818142 6.12680081 6.01031782 5.86216377 6.25899993 5.89568929\n 5.68632827 6.30867067 5.46927543 6.19284142 6.35492381 6.95086733\n 6.43163924 5.72412356 5.32300576 6.75124195 5.3625749  6.69331882\n 6.26641518 5.89412968 6.99898723 6.52522852 7.19822269 5.46939585\n 7.25904355 7.34083757 5.43615261 5.73896675 6.25869582 5.99653938\n 7.52523591 6.59985076 6.15610344 6.31525566 5.77905914 8.1191844\n 6.00834192 6.03039971 6.49116591 6.53260274 5.5194413  5.33583868\n 6.26602785 5.59052746 5.11685748 6.50918853 5.30696434 6.44742103\n 5.64867264 6.42275366 6.04348893 5.6817272  7.00711979 5.9037828\n 5.71580853 7.02606981 5.56890355 6.68211868 5.98926826 7.02561504\n 7.07845365]\n"
     ]
    }
   ],
   "source": [
    "some_data = X\n",
    "some_labels = y\n",
    "print('Predictions:', lin_reg.predict(some_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Labels: [4.6, 6.6, 9.1, 5.1, 5.3, 4.6, 6.3, 8.1, 4.7, 5.2, 7.8, 4.6, 5.9, 5.7, 7.8, 5.4, 6.2, 4.6, 4.9, 7.8, 5.6, 4.8, 5.5, 4.9, 5.1, 5.5, 4.9, 4.8, 5.6, 5.6, 7.0, 7.6, 5.3, 5.3, 6.4, 6.8, 7.2, 5.9, 7.1, 5.1, 5.7, 8.3, 7.6, 4.5, 6.1, 6.1, 6.1, 7.6, 6.6, 5.2, 8.0, 7.8, 4.4, 4.4, 9.1, 5.5, 7.0, 10.0, 8.3, 7.6, 7.2, 5.1, 5.8, 4.8, 5.1, 5.3, 5.6, 5.8, 4.9, 6.8, 5.5, 4.9, 5.0, 4.9, 4.3, 4.4, 7.7, 5.2, 5.9, 7.4, 6.2, 5.3, 5.4, 6.2, 7.6, 4.8, 8.4, 5.2, 6.0, 4.9, 6.4, 8.5, 5.5, 4.9, 4.3, 5.9, 6.3, 5.9, 8.8, 4.8, 4.9, 5.5, 5.3, 4.9, 6.4, 6.5, 5.0, 8.1, 6.6, 6.2, 4.5, 6.9, 8.3, 5.7, 4.7, 4.8, 5.4, 7.7, 4.6, 5.3, 4.5, 4.2, 4.8, 8.1, 4.3, 4.8, 6.9, 4.6, 7.6, 4.3, 4.9, 5.3, 4.9, 4.9, 7.1, 5.2, 6.0, 4.7, 5.3, 4.4, 9.8, 8.5, 6.9, 4.7, 5.1, 5.3, 6.6, 5.4, 6.8, 5.7, 5.1, 4.8, 7.9, 4.9, 6.2, 6.9, 7.9, 4.9, 6.3, 6.7, 4.9, 8.2, 7.0, 7.2, 6.7, 6.6, 9.3, 6.6, 6.1, 7.3, 4.8, 4.7, 5.3, 5.2, 7.1, 5.8, 7.6, 5.3, 8.3, 9.0, 4.7, 5.9, 10.0, 6.7, 4.9, 5.8, 6.7, 7.3, 5.2, 6.9, 4.5, 4.9, 6.0, 7.7, 5.3, 4.8, 8.0, 5.1, 5.7, 7.3, 4.3, 4.8, 6.9, 9.0, 8.8]\n"
     ]
    }
   ],
   "source": [
    "print(\"Labels:\",list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(range(len(some_labels)), some_labels, c='r', s=2)\n",
    "plt.scatter(range(len(some_labels)), lin_reg.predict(some_data), c='g',s=2)\n",
    "plt.bar(range(len(some_labels)), some_labels-lin_reg.predict(some_data), linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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n1+q4zHqpW+cK+suhnwzMAF+TdAYwBXwmIl6Zt8848My8n3e3tj03/4UkrWW2B88JJ5ywgGa/oezqgrzlaMMYHKtTrnaQ85/V/jodl1kvdRwIV/SoX5Y0ATwErIyILZJuBF6OiL+Zt893gPUR8aPWz/8J/HVETGa97sTERExOZj6dS5lVLu2DfjAbsNx7fEOdqozMmk7SVERMdHqunx76bmB3RGxp/XwnsK5tn2ng+Hk/H9faNhRl9uzq+C1cN6n0rP3FY03XM6BHxC8kPSNpeUTsAFYBP23b7R7gk5LuAM4BXoqI59pfK1WpBCzLVseaYbOi9VuH/ingG5IOBZ4ErpJ0NUBEbADuAy4GdgJ7gatKaGsyUu8J5m1/Csdbt5rhFM6ZpaevgB4R24H2nM2Gec8HcE1xzUpX6j3BvO1P5XjrVDOcyjmz9CQzH3oqUl+1JW/7UzneOtUMp3LOLD0O6AWrU09wEHnbn8rx1qlmOJVzZulJfi6XuilyGtUq8qx52z/I8VZxXHWqVqrbVLvWHO6hF6yonmBVS5rlbX/e/atcqq3MJejyqNPVgjWLe+gL0K2nudCeYFVVGXnb323/TuenbtUmVajT1YI1S887RctS5J2iRcmTCijyDtJO73vtt7bT6Tcj4Kn1H8n1+lXIOj/twXxOKsdlxXL5Zn4LvVO0cTp9iIBcpWS9KhUG/WKYe98jM1YmSiXPmnV+stZETeW4rDgu3yzeyAX0rA/RYUsOyZUKyKpImHu9hX4xHLbkkIN6tEXnWcvsHXVbyKLs47I0OP1WvJEbFM36EL2YseJPVmDqtlBDnhrjrNffs3d/qdPJlj04mXV+5o7D0+SayzeLN3I99LwflqzAlDVtbFaOuNsXQ1YJW5lzyJTdO+o2ra7nxjFw+WYZRq6HnvVhWTq2JFcpWdZCDW87fEnn18/YXlUJW9m9o7otxGH14/LN4o1cDz2r5/j5S08D8pWSdeppfv6exzrum1VMtHrFOJO7fv36qveLJD56Vvk92GH0jrJ64q5sMHD5ZhlGLqD3+hAt9MP0UofKlG7bN22b5q6p6dcrPw5EcNfUNBMnvr3UD3bZKz1lcWWDzef0W7FGLqBDuR+ivD3fVG4gKoorG8zK0+iAXsWlfd6eb5Uj/VX0jlzZYFaexg6KVjVnSN7BwDpN6zoMo3a8ZsPU2B56lZf2eXq+VeWyqzJqx2s2TI0N6Klc2o/aSP+oHa/ZMDU2oKd000LeXHbqZX+ubDArR2MDepGX9nUKoC77szLU6TNug2vsoGhRdypWuSBDJ16P0opWt8+4Da6xPXQo5tK+bnXTqYwNWDrq9hm3wTU6oBehbgG029iAL5ttEHX7jNvgGptyKUrd6qazJjQ6/9Rlvmy2gdTtM26D6yugS/q5pEckbZd00Lpxkj4g6aXW89slfa74plajbjPCZY0NfP+JGefWbSB1+4zb4PKkXM6PiOe7PP/DiLhkoQ2qmzrWTXcaG7j2W9s77uvLZuuljp9xG4xz6H1IoW46pbp7qx9PddwM/ebQA3hQ0pSktRn7nCvpYUnflXRapx0krZU0KWlyZmZmoAZbZ75stqK5nDE9/fbQ3x8R05LeAXxP0hMR8YN5z28FToyI30q6GNgEnNL+IhFxC3ALwMTERMaSD9XJ2xupU+/Fl81WNJczpqevgB4R062/fyXpbuBs4Afznn953uP7JH1V0tE9cu61kvcOzDresVlVaqhOX2xWHJczpqdnykXSEZLeMvcY+DDwaNs+75Kk1uOzW6/7QvHNLU/eOzAHuWNz07ZpVq7fzMnr7mXl+s2NuHT1ZXlzuZwxPf3k0N8J/EjSw8D/APdGxP2SrpZ0dWufK4BHW/t8BVgTkbWKZj3l7Y3k3d7UwOepCJrL4zLp6ZlyiYgngTM6bN8w7/FNwE3FNm248laJpLLUXNl8Wd5cHpdJj8sWW/LOzpjSUnNlcrlks6VQsmtvcEBvydsbybt/EwJfp8FPr0BkVh+qKtU9MTERk5MHzSLQWO1VMTAb+AaZ0rcK3doPviw3GxZJUxEx0em5keyhV1Fml3o+stsYwI/XXZDMcZg12cgF9Crrx1PORzZ1DMCsSUZu+lyX2Q3GNclm9TdyAd09zcG4Jtms/kYuoLunOZii1mg1s/KMXA7dZXaDS3kMwGwUjFxAT73axMwsy8gFdHBP08yaaeRy6GZmTeWAbmbWEA7oZmYNMZI59FHjFYXMRoMDesPVcak8MyuHA3qNFdGzburCGmZ2MAf0miqqZ+2pDsxGhwdFa6qoScQ81YHZ6HBAr6mietaeVMtsdDig11RRPWtPqmU2OpxDr6kiJxHzVAdmo8EBvaY8iZiZ5dVXQJf0c+A3wAHgtfYFSiUJuBG4GNgL/ElEbC22qaPHPWszyyNPD/38iHg+47mLgFNaf84Bbm79bWZmQ1LUoOhlwNdj1kPAUknHFPTaZmbWh34DegAPSpqStLbD8+PAM/N+3t3aZmZmQ9JvyuX9ETEt6R3A9yQ9ERE/yPtmrS+DtQAnnHBC3n9uZmZd9NVDj4jp1t+/Au4Gzm7bZRo4ft7Px7W2tb/OLRExERETy5YtG6zFZmbWUc8euqQjgEMi4jetxx8GvtC22z3AJyXdwexg6EsR8Vy3152amnpe0q7Wj0cDWQOuTeTjbbZROt5ROlaox/GemPVEPymXdwJ3z1Ymshj4ZkTcL+lqgIjYANzHbMniTmbLFq/q9aIR8XoXXdJkeylkk/l4m22UjneUjhXqf7w9A3pEPAmc0WH7hnmPA7im2KaZmVkensvFzKwh6hLQb6m6AUPm4222UTreUTpWqPnxajZbYmZmqatLD93MzBbIAd3MrCFqEdAlLZK0TdJ3qm5L2ST9XNIjkrZLmqy6PWWStFTSnZKekPS4pHOrblNZJC1v/U7n/rws6bNVt6tMkq6V9JikRyXdLumwqttUFkmfaR3nY3X+vdZlPvTPAI8Db626IUPSbebKJrkRuD8irpB0KHB41Q0qS0TsAN4Dsx0UZu+UvrvKNpVJ0jjwaeAPI2KfpH8D1gD/XGnDSiDp3cCfM3uH/KvA/ZK+ExE7q23ZwSrvoUs6DvgIcGvVbbHiSDoSOA+4DSAiXo2IPZU2anhWAT+LiF0990zbYmBM0mJmv6yfrbg9ZfkDYEtE7I2I14D/Ai6vuE0dVR7QgX8A/gr4XcXtGJZeM1c2xcnADPC1Vjrt1tbUEaNgDXB71Y0oU2t+py8DTwPPMTvdx4PVtqo0jwJ/JOkoSYcze1f88T3+TSUqDeiSLgF+FRFTVbZjyN4fEWcyuyjINZLOq7pBJVkMnAncHBErgFeAddU2qXyt1NKlwLerbkuZJL2N2XUQTgaOBY6Q9LFqW1WOiHgc+BLwIHA/sJ3Z1dtqp+oe+krg0tYSd3cAF0j612qbVK4+Zq5sit3A7ojY0vr5TmYDfNNdBGyNiF9W3ZCSfRB4KiJmImI/sBF4X8VtKk1E3BYRZ0XEecCLwP9W3aZOKg3oEXF9RBwXEScxe5m6OSIa+S0PszNXSnrL3GNmZ658tNpWlSMifgE8I2l5a9Mq4KcVNmlYrqTh6ZaWp4H3Sjq8tabwKmYLGxqptRYEkk5gNn/+zWpb1FldqlxGRceZK6ttUqk+BXyjlYZ4kj5m4UxZ60v6Q8DHq25L2SJii6Q7ga3Aa8A2an5b/ALdJekoYD9wTV0H+H3rv5lZQ1SdQzczs4I4oJuZNYQDuplZQzigm5k1hAO6mVlDOKCbmTWEA7qZWUP8PwPR5NC2mDfoAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "predict_y = lin_reg.predict(test_X)\n",
    "plt.scatter(test_y,predict_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clarke网格误差分析\n",
    "def clarke(y,y_predicted):\n",
    "    \"\"\"\n",
    "    clarke网格分析\n",
    "\n",
    "    total, percentage = clarke(y, yp)\n",
    "    \n",
    "    INPUTS:\n",
    "    y       Reference values(血糖浓度参考值)\n",
    "    yp      Predicted/estimated values(血糖预测值)\n",
    "    \n",
    "    OUTPUTS:\n",
    "\n",
    "    \"\"\"\n",
    "    # 设置坐标轴范围和坐标轴名称\n",
    "    plt.xlim(0, 400*0.0556)\n",
    "    plt.ylim(0, 400*0.0556)\n",
    "    plt.xlabel('Reference values of Blood Glucose(mmol/L)')\n",
    "    plt.ylabel('Predicted values of Blood Glucose(mmol/L)')\n",
    "    # 获取数据长度\n",
    "    n = len(y)\n",
    "    # 散点图绘制数据\n",
    "    plt.scatter(y, y_predicted, s=15)\n",
    "    # 绘制网线\n",
    "    # 上半区网线\n",
    "    plt.plot([0,400*0.0556],[0,400*0.0556],linestyle=':',c='k') \n",
    "    plt.plot([0,175*0.0556/3],[70*0.0556,70*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([175*0.0556/3,400*0.0556/1.2],[70*0.0556,400*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([70*0.0556,70*0.0556],[84*0.0556,400*0.0556],c='k', linewidth=1)\n",
    "    plt.plot([0,70*0.0556],[180*0.0556,180*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([70*0.0556,290*0.0556],[180*0.0556,400*0.0556],c='k', linewidth=1)\n",
    "    # 下半区网线绘制\n",
    "    plt.plot([70*0.0556,70*0.0556],[0,56*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([70*0.0556,400*0.0556],[56*0.0556,320*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([180*0.0556,180*0.0556],[0,70*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([180*0.0556,400*0.0556],[70*0.0556,70*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([240*0.0556,240*0.0556],[70*0.0556,180*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([240*0.0556,400*0.0556],[180*0.0556,180*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([130*0.0556,180*0.0556],[0,70*0.0556],c='k',linewidth=1)\n",
    "    \n",
    "    # 绘制区域标签A,B,C,D,E\n",
    "    plt.text(30*0.0556,20*0.0556,'A',fontsize=9);\n",
    "    plt.text(30*0.0556,150*0.0556,'D',fontsize=9);\n",
    "    plt.text(30*0.0556,380*0.0556,'E',fontsize=9);\n",
    "    plt.text(150*0.0556,380*0.0556,'C',fontsize=9);\n",
    "    plt.text(160*0.0556,20*0.0556,'C',fontsize=9);\n",
    "    plt.text(380*0.0556,20*0.0556,'E',fontsize=9);\n",
    "    plt.text(380*0.0556,120*0.0556,'D',fontsize=9);\n",
    "    plt.text(380*0.0556,260*0.0556,'B',fontsize=9);\n",
    "    plt.text(280*0.0556,380*0.0556,'B',fontsize=9);\n",
    "\n",
    "    # 计算数据\n",
    "    total = np.zeros(5)\n",
    "    # A区域\n",
    "    for i in range(0,n):\n",
    "        if (y[i] <= 70*0.0556 and y_predicted[i] <= 70*0.0556) or (y_predicted[i] <= 1.2*y[i] and y_predicted[i] >= 0.8*y[i]): \n",
    "            total[0] = total[0] + 1\n",
    "        else:\n",
    "            # E区域\n",
    "            if(y[i] >= 180*0.0556 and y_predicted[i] <= 70*0.0556) or (y[i] <= 70*0.0556 and y_predicted[i] >= 180*0.0556):\n",
    "                total[4] = total[4] + 1\n",
    "            else:\n",
    "                # 区域C\n",
    "                if (y[i] >= 70*0.0556 and y[i] <= 290*0.0556) and (y_predicted[i] >= y[i] + 110*0.0556) or (y[i] >= 130*0.0556 and y[i] <= 180*0.0556) and (y_predicted[i] <= (7/5)*y[i] - 182*0.0556):\n",
    "                    total[2] = total[2] + 1\n",
    "                else:\n",
    "                    # 区域D\n",
    "                    if (y[i] >= 240*0.0556) and (y_predicted[i] >= 70*0.0556) and (y_predicted[i] <= 180*0.0556) or (y[i] <= 175*0.0556/3 and y_predicted[i] <= 180*0.0556) and (y_predicted[i] >= 70*0.0556) or (y[i] >= 175*0.0556/3 and y[i] <= 70*0.0556) and (y_predicted[i] >= (6/5)*y[i]):\n",
    "                        total[3] = total[3] + 1\n",
    "                    else:\n",
    "                        # 区域B\n",
    "                        total[1] = total[1] + 1\n",
    "\n",
    "    percentage = (total/n)*100\n",
    "    plt.show()\n",
    "    return total, percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "total, percentage = clarke(test_y, predict_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([54., 28.,  0.,  0.,  0.])"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "source": [
    "数据点基本落入克拉克网络A,B区域，但是在B区域的点有点过多，准确率不是很高"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([65.85365854, 34.14634146,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "percentage"
   ]
  },
  {
   "source": [
    "## 偏最小二乘回归实现"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_decomposition import PLSRegression\n",
    "\n",
    "pls2 = PLSRegression(n_components=2)\n",
    "pls2.fit(train_X, train_y)\n",
    "predict_y_pls2 = pls2.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "total_pls2, percentage_pls2 = clarke(test_y, predict_y_pls2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([54., 28.,  0.,  0.,  0.])"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "total_pls2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([65.85365854, 34.14634146,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "percentage_pls2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rmse(yi, y):\n",
    "    sum_val = ((yi-y)**2).sum()\n",
    "    return np.sqrt(sum_val/len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1.1626573090066017"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "get_rmse(predict_y, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}